CERES: Advanced planning for the energy transition

Introduction

As power systems undergo rapid transformation toward decarbonization, planning tools must evolve to deliver timely, accurate, and technically robust insights. CERES (Chronological Energy Resource Expansion Simulator) is a computational framework for grid expansion planning. By employing full chronological modeling, proprietary numerical acceleration techniques, and cloud-based execution, CERES supports high-fidelity simulation and optimization of future energy systems involving variable renewables, storage, transmission, hydrogen, and natural gas infrastructure.

This document presents CERES’s capabilities across six infrastructure domains—generation, storage, transmission, distribution, hydrogen, and natural gas—and outlines how it addresses critical modeling challenges, including stochastic optimization, renewable and demand volatility and more.

1. Comprehensive Infrastructure Modeling

CERES incorporates all key elements of modern energy systems:

Generation:

  • Models dispatchable (thermal, hydro, nuclear) and non-dispatchable (solar, wind) generation.

  • Captures unit-level constraints: start-up costs, ramping, minimum up/down times, temperature dependencies.

  • Considers thermal unit retirement strategies based on techno-economic criteria or policy mandates.

Storage:

  • Models various storage technologies including batteries, pumped hydro, and emerging chemistries.

  • Accounts for charge/discharge efficiency, degradation, depth of discharge, and lifespan.

  • Optimizes storage capacity and dispatch using chronological simulation.

Transmission:

  • Supports nodal and zonal representations with AC or DC network modeling.

  • Determines optimal expansion plans based on system reliability and cost trade-offs.

  • Accounts for grid reinforcement, congestion relief, and line rating variations.

Distribution:

  • Represents medium- and low-voltage network impacts, including backfeed and congestion.

  • Captures dynamics of distributed resources and demand-side flexibility.

Hydrogen:

  • Models electrolyzer investment and operation within the broader energy system.

  • Represents sector coupling between electricity and hydrogen.

  • Includes hydrogen storage and network expansion scenarios.

Natural Gas:

  • Couples gas generation with upstream infrastructure.

  • Represents gas network elements such as pipelines and storage.

  • Allows modeling of fuel switching and emissions scenarios.

2. Advanced Chronological Modeling

Full Chronology: CERES simulates each hour (or sub-hour interval) over planning horizons up to 30 years. This method:

  • Retains intra- and inter-annual variability.

  • Accurately captures storage cycling, curtailment, and system reliability needs.

  • Eliminates biases introduced by representative aggregation such as those introduced by load blocks or typical days.

Typical Days Option: For exploratory or preliminary analysis, CERES supports the use of typical days or representative periods. This reduces computational demand at the expense of some temporal accuracy.

3. Capacity additions and retirements

Interests During Construction (IDCs): CERES computes IDCs based on interest rates, and capital expenditures:

  • Enables incorporation of financing costs into investment planning.

  • Supports phased buildout evaluation with differential timing.

Asset Lifetimes and Truncated Horizons: When asset lives exceed the planning horizon CERES applies cost amortization or salvage value assumptions.

Retirement Modeling: Retirements are endogenously determined based on:

  • Emissions or policy constraints.

  • Maintenance and operating costs.

  • Marginal cost comparison with new assets.

4. Stochastic Optimization

CERES incorporates stochastic optimization techniques to reflect uncertainty in:

  • Renewable generation profiles (e.g., interannual solar and wind variability).

  • Fuel price trajectories (e.g., global gas market fluctuations).

  • Demand projections under electrification scenarios.

  • Hydrological variability for hydro-dominated systems.

Modeling Approach:

  • Scenario-based stochastic programming with probabilistic weighting.

  • Decomposition and parallel solution algorithms improve tractability.

  • Includes recourse strategies such as storage dispatch and demand curtailment.

Outcome:

  • Delivers robust investment portfolios under uncertain conditions.

  • Identifies critical flexibility assets across scenarios.

5. Computational Acceleration and Cloud Architecture

CERES uses Distributed Incremental Acceleration, a proprietary technique to enable large-scale simulations with efficient parallel processing:

  • Cloud-native deployment allows dynamic CPU resource scaling.

  • Enables interactive and iterative planning cycles with short turnaround times.

  • Employs secure protocols: encryption, anonymization, and multifactor authentication.

Conclusion

CERES provides a rigorous analytical platform for integrated, high-resolution energy system planning. Its modeling framework spans generation, storage, transmission, distribution, hydrogen, and gas, and its capabilities extend to stochastic uncertainty, and full chronology representation to capture renewable volatility. With rapid cloud-based computation, CERES facilitates technically sound and economically robust expansion planning in complex, decarbonizing energy systems.